### Table 1: Clustering Criterion Functions.

2002

"... In PAGE 3: ... For those partitional clustering algorithms, the clustering problem can be stated as computing a clustering solution such that the value of a particular criterion function is optimized. In this paper we use six different clustering criterion functions that are defined in Table1 and were recently compared and analyzed in a study presented in [45]. These functions optimize various aspects of intra-cluster similarity, inter-cluster dissimilarity, and their combinations, and represent some of the most widely-used criterion functions for document clustering.... ..."

Cited by 69

### Table 1: Clustering Criterion Functions.

2002

"... In PAGE 3: ... For those partitional clustering algorithms, the clustering problem can be stated as computing a clustering solution such that the value of a particular criterion function is optimized. In this paper we use six different clustering criterion functions that are defined in Table1 and were recently compared and analyzed in a study presented in [45]. These functions optimize various aspects of intra-cluster similarity, inter-cluster dissimilarity, and their combinations, and represent some of the most widely-used criterion functions for document clustering.... ..."

Cited by 69

### Table 2. Clustering results after fitting various number of components (k) and for each dietary quality group comparisona

"... In PAGE 8: ... Hence, LOW is clearly different from HIGH and MED, which just differ in degree. Model-Based Clustering and ANOVA Results A summary of the mixture model fitting results is given in Table2 , where the values for the various model selection criteria (AIC, BIC, and LRT) are presented for each model ranging from one to five components. For all three comparisons, there was a dramatic decrease in both AIC and BIC, along with an increase in the log- likelihood when moving from a mixture model with one component to a model with two components.... ..."

### Table 4 Illustration of phonological dissimilarity measurement with PSIMETRICA for the words placemats and amount . Word Phonological Dissimilarity Mean Dissimilarity

2003

"... In PAGE 9: ... Illustrative example. To illustrate how PSIMETRICA may be applied, let us again consider the words place- mats and amount ( Table4 ). For these words, the syllables, phoneme clusters, and phonemes must first be aligned in the manner explained previously (cf.... In PAGE 9: ... When the phonemes of a pair are both non- null, this value equals the proportion of non-null features that differ between the phonemes. These proportions appear as fractions in Table4 . For example, the phonemes /e/ and /a0 / have nine relevant features, and only two of these features are different.... In PAGE 9: ... Dissimilarity values for pairs of null phonemes are not included in this average. These mean dis- similarity values for the clusters of placemats and amount appear in the right-most column of Table4 . For example, the mean dissimilarity value of the second nuclei in placemats and amount is 0.... ..."

Cited by 4

### Table 14 Five Methods Used With Isolated Cultures

1994

"... In PAGE 24: ...pond and react. This undoubtedly skewed our results. (Sorenson, 1976, pp. 139-140) Four principal methods were used to gather data from four separate cultures, as outlined in Table14 . I consider each method in turn.... ..."

Cited by 26

### Table 1: Dissimilarity measures currently implemented in ECLEST. All parameters are speci ed in a con guration le.

"... In PAGE 13: ... The interfaces are designed in such a way that ECLEST can be easily extended by new algorithms of any of the three categories. In the current version, ve dissimilarity measures have been implemented, which are listed in Table1 , one clustering algorithm and one evaluation method. Currently, only the single linkage clustering algorithm has been implemented.... ..."

### Table 2. Degrees of Freedom for an Isolated Tray New Variables New Equations

1995

"... In PAGE 9: ... A standard distillation tray has two input streams and two output streams, as shown in Figure 3. The four streams introduce a net of 4(nc + 2) new variables, which is the first entry on Table2 . By keeping track of the number of variables and equations introduced by each new element of the model, we can determine the degrees of freedom, and how many variables must be fixed to obtain a system with the same number of equations and free variables.... In PAGE 10: ... Diagram of tray and equation 7 once. As shown in Table2 , the component material balances therefore introduce nc equations. There are nc + 2 equilibrium equations.... ..."

Cited by 2

### Table 1: PoCluster generated based on dissimilarity ma- trix in Figure 3(B).

### Table 1: A procedure for creating a `dissimilarity apos; ordering of data.

1996

"... In PAGE 6: ... Taking the lead from Cluster#2F2, a measure-independent idea #0Crst sorts using a random data ordering, then extracts a biased `dissimilarity apos; ordering from the hierarchical clustering, and sorts again. The function of Table1 outlines the reordering procedure. It recursively extracts a list of observations from the most probable #28i.... ..."

Cited by 7

### Table 1 Material parameters in fatigue damage criterion

2005

"... In PAGE 4: ... 2). After obtaining the new O102030 coordinate system, we can calculate the critical plane based on Table1 . For different materials, check the a value from Table 1.... In PAGE 4: ...rthogonal coordinate system (Fig. 2). After obtaining the new O102030 coordinate system, we can calculate the critical plane based on Table 1. For different materials, check the a value from Table1 . Rotate O102030 about 30 axis by an angle of a degrees to be the new coordinate system O123 (Fig.... ..."